Strategizing your Predictive Maintenance Journey

By Admin,

With the increasing adoption of Digital Transformation and Industry 4.0, it's nearly impossible to talk about it without considering Predictive Maintenance. Be it Oil & Gas, Chemicals, Metals-Mining, renewables, or the Bio-pharma industry - everyone starts the journey with the implementation of Predictive Maintenance. Being an essential part of Digital transformation, it can be a simple plug-and-play solution, it needs to be a journey for successful implementation and realization. In this article, we will focus on what it takes for the effective implementation of such solutions. To know more about Digital transformation in Process and Operations kindly have a look at one of my earlier blogs:


Digital Transformation – Revolutionizing the process Industry

Introduction “Technology is best when it brings people together” “Technology is best when you use it wisely” “Data is the real evidence” That’s right

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1. Introduction

This new way or approach starts with the implementation of condition-based monitoring (CBM) solutions, where these conditions would be far more complex and reliable when compared to the simple rule-based approach. CBM can be extended further to correlate with the possible faults to develop the scoring metric, which could be used as the early warning indicator to foresee any potential failures. To know more about Condition Based Monitoring kindly have a look into the below blog:

These scoring metrics could be interpreted as the number that translates the data into actionable insights for shop-floor consumption and corrections.


2. Strategy for Predictive Maintenance

Strategizing the implementation plan for predictive maintenance is the most important phase for realizing success and for achieving the business KPIs. An effective strategy can help the organization to optimize thier CapEx and OpEx, with complete utilization of the asset's life-cycle.

1673070717120Following would be the steps for implementation of PdM:

1.    Strategic mapping of the predictive maintenance solution would start with the cultural transformation as the very first step, where identification of the key skill sets and the right workforce would dictate the overall implementation.

2.    Following this, the second step would be to estimate the total number of assets to be considered and rank them as per their criticality based on their downtime/failure impact on the overall production and business. (This can be fetched from the FMECA reports)

3.    Further to this exercise, the third (next logical) step would be to estimate and develop the list of possible failures which can lead to abnormalities or unwanted stoppages of the assets.

4.    Fourth step would be to estimate the available tags/data and assess the overall integrity of the data. With this information, the workforce would utilize their domain and shop-floor experience to map the identified failures with the available data, which would essentially guide the organization to realize the class of failures which could be predicted using the available dataset.

5.    Next step, which will be the fifth one, will be to pin down any requirement for additional sensors or the soft-sensors which would be required to correlate with the failures, which could have not been mapped earlier with the available dataset.

6.    Based on this step, the organization can take the decision to fulfill the requirements by implementing the additionally identified sensors.
7.    Selection of the right solution, which would fulfill the requirements

8.    Development of predictive maintenance models/templates using the identified solution.

9.    Development of the consumption layer: In this step, the scalable dashboards shall be built in order to convert the templates into actionable insights, which could be consumed by the workforce team to take corrective action in real time.

10. Validation of the predictive maintenance models for the estimated duration

11. Scaling the predictive maintenance models across all the similar assets

12. Management and model fine-tuning over the course of time with more data and additional failure scenarios - Iterative

It's been observed that the industries which have been able to successfully follow the above steps have realized the benefits and savings - Operational excellence, cost reduction, and others.

If you are interested to know more and understand its relevance in your industry, please reach out to us at:


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